Basis Function Neural Network Image Restoration Algorithm

Image restoration is one of the important research topics in the field ofimage processing. It has important practical significance and application valuein military remote sensing, Road traffic, detection and medical imaging and soon. The technology of image restoration technology belongs to imagepreprocessing, but the fundamental part. The results of the restoration of digitalimages play an important role in the further research. However, it would bedegraded when we obtain the image in reality. So how to recover the degradedimages is becoming a hot issue in recent years.Image degradation is caused by complex and diverse factors, and the pointspread function is difficult to determine. According to the fact that point spreadfunction of the degraded image can't obtain accurately, basis function neuralnetwork for image restoration was constructed based on the Orthogonalpolynomial basis functions in this paper, The hidden-layer neurons are activatedby a series of orthogonal functions, update its weights by the errorBack-propagation training algorithm and finally reached convergence target. Asthe topology of the neural network take a great impact on its performance, thispaper determine the number of the hidden layer neurons which under the bestperformance in a given error using hidden-neuron growing algorithm. To avoidlengthy BP-training of the iterative weights-up dating, this paper found therelationship between the number of iterative training and the weights,weights-direct-determination algorithm for image restoration was proposed,Compared with traditional algorithms, it is has good results.Finally, A new method for region extraction of partial blurred image ispresented based on the Fourier spectrum amplitudes, this method can avoid theaffect of background area, realize the extract blur region well compare to thetraditional methods.